Implant Treatment in Patient with Periodontitis: A Case Report with 13-year Follow-up.

This report describes long-term implant treatment in a patient with chronic periodontitis. The patient was a 59-year-old man who attended our facility requesting a dental implant. An initial examination revealed generalized gingival inflammation and subgingival calculus. Clinical examination revealed 55.3% of sites with a probing depth (PD) of >4 mm and 41.3% of sites with bleeding on probing. Radiographic examination revealed vertical bone resorption in #23, #33, #33, #35, and #47. Initial periodontal therapy consisting of plaque control, scaling and root planing, and tooth extraction was subsequently performed based on a clinical diagnosis of severe chronic periodontitis. Open flap debridement was performed for teeth with a PD >5 mm (#21, #22, #23, 333, #34, #35 and #47). After confirming the stability of the periodontal tissue, 3 implants were first placed in the maxilla (#25, #26, and #27). Final prostheses comprising a screw retaining-type implant superstructure were then placed (#25, #26, and 327). Following reevaluation, the patient was placed on supportive periodontal therapy. At 15 years after the first visit, the periodontal and implant conditions have remained stable. These results indicate that periodontal treatment before implantation and subsequent maintenance yield a clinically favorable and long-lasting outcome.

Regression analysis for predicting the elasticity of liquid crystal elastomers.

It is highly desirable but difficult to understand how microscopic molecular details influence the macroscopic material properties, especially for soft materials with complex molecular architectures. In this study we focus on liquid crystal elastomers (LCEs) and aim at identifying the design variables of their molecular architectures that govern their macroscopic deformations. We apply the regression analysis using machine learning (ML) to a database containing the results of coarse grained molecular dynamics simulations of LCEs with various molecular architectures. The predictive performance of a surrogate model generated by the regression analysis is also tested. The database contains design variables for LCE molecular architectures, system and simulation conditions, and stress-strain curves for each LCE molecular system. Regression analysis is applied using the stress-strain curves as objective variables and the other factors as explanatory variables. The results reveal several descriptors governing the stress-strain curves. To test the predictive performance of the surrogate model, stress-strain curves are predicted for LCE molecular architectures that were not used in the ML scheme. The predicted curves capture the characteristics of the results obtained from molecular dynamics simulations. Therefore, the ML scheme has great potential to accelerate LCE material exploration by detecting the key design variables in the molecular architecture and predicting the LCE deformations.